When we do radioactive work, our data can be pretty scattered. We have an internal policy to eliminate up to 1/3 of the data, but we just eye-ball the bad points. We want a systematic, and more importantly, a verified (or at least an established) method to eliminate the outliers. Most methods (Q-test, etc. ) only eliminate one point, but I did discover the Peirce's Criterion that is supposedly a valid way to identify and eliminate multiple points.

However, I can't see too many people using this method (there is a paper from NASA that talks about the virtues of this method, but the boss wants to see the method used in biotech, or at least in radiation work). If anyone uses that method, or knows of another method that is commonly used for multiple outliers, please let me know!

-hyphae-

Would an alternate approach, given that you are loosing a third of your data, be to look for a transformation to normalise the data? That way you wouldn’t need to eliminate outliers.